A New Unbiased Estimator of MLR Model Coefficients Based on AC

Section: Research Paper
Issue
Vol. 23 No. 1 (2026): Volume 23 Issue 1
Pages
163-173

Abstract

One of the well-known statistical methods in predictive analysis is the use of the multiple linear regression (MLR) model. In many studies, various estimators have been proposed to estimate the coefficients of this type of regression model, of which the ordinary least squares (OLS) estimator is one of the most famous and, at the same time, is one of the most accurate. This paper introduces a new estimator of MLR model coefficients based on autocovariance (AC). It is shown that although the AC-based estimator proposed in this paper may not be intuitively appealing, it is an unbiased estimator of the model coefficients. It is also shown that if the vector of independent variables satisfies certain regularity conditions, under the weak condition that the error terms follow an autoregressive moving average (ARMA) model, this estimator has the same asymptotic probability distribution as the LS estimator and converges probabilistically to the model coefficients. Finally, a simulation study confirms that the mentioned properties of the new AC-based estimator hold true even in small samples.

Keywords:

MLR model; Unbiased estimator; AC-based estimator; LS estimator; ARMA model.

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Author

Sajjad Piradl

Identifiers

DOI: 10.33899/iqjoss.v23i1.62136

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A New Unbiased Estimator of MLR Model Coefficients Based on AC. (2026). IRAQI JOURNAL OF STATISTICAL SCIENCES, 23(1), 163-173. https://doi.org/10.33899/iqjoss.v23i1.62136

How to Cite

A New Unbiased Estimator of MLR Model Coefficients Based on AC. (2026). IRAQI JOURNAL OF STATISTICAL SCIENCES, 23(1), 163-173. https://doi.org/10.33899/iqjoss.v23i1.62136